summary.gls               package:nlme               R Documentation

_S_u_m_m_a_r_i_z_e _a _g_l_s _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Additional information about the linear model fit represented by
     'object' is extracted and included as components of 'object'. The
     returned object is suitable for printing with the
     'print.summary.gls' method.

_U_s_a_g_e:

     ## S3 method for class 'gls':
     summary(object, verbose, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object inheriting from class 'gls', representing a
          generalized least squares fitted linear model.

 verbose: an optional logical value used to control the amount of
          output in the 'print.summary.gls' method. Defaults to
          'FALSE'.

     ...: some methods for this generic require additional arguments. 
          None are used in this method.

_V_a_l_u_e:

     an object inheriting from class 'summary.gls' with all components
     included in 'object' (see 'glsObject' for a full description of
     the components) plus the following components: 

 corBeta: approximate correlation matrix for the coefficients estimates

  tTable: a data frame with columns 'Value', 'Std. Error', 't-value',
          and 'p-value' representing respectively the coefficients
          estimates, their approximate standard errors, the ratios
          between the estimates and their standard errors, and the
          associated p-value under a t approximation. Rows correspond
          to the different coefficients.

residuals: if more than five observations are used in the 'gls' fit, a
          vector with the minimum, first quartile, median, third
          quartile, and maximum of the residuals distribution; else the
          residuals.

     AIC: the Akaike Information Criterion corresponding to 'object'.

     BIC: the Bayesian Information Criterion corresponding to 'object'.

_A_u_t_h_o_r(_s):

     Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates
     bates@stat.wisc.edu

_S_e_e _A_l_s_o:

     'gls', 'AIC', 'BIC', 'print.summary.gls'

_E_x_a_m_p_l_e_s:

     data(Ovary)
     fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
                correlation = corAR1(form = ~ 1 | Mare))
     summary(fm1)

